KR-20260065472-A - METHOD AND DEVICE FOR PROVIDING LAB ASSESSMENT SERVICES, AND METHOD FOR TRAINING NEURAL NETWORKS TO PROVIDE LAB ASSESSMENT SERVICES

KR20260065472AKR 20260065472 AKR20260065472 AKR 20260065472AKR-20260065472-A

Abstract

A method and apparatus for providing a laboratory evaluation service, and a method for learning a neural network for providing a laboratory evaluation service are disclosed. A method for providing a laboratory evaluation service according to one embodiment may include an operation of calculating an evaluation score for each of the plurality of laboratories based on paper information of the plurality of laboratories belonging to each of the plurality of universities and value information for each academic society corresponding to the value of the plurality of academic societies that publish the papers, and an operation of providing a list showing the ranking of the plurality of laboratories based on the score of each of the plurality of laboratories.

Inventors

이지우

Assignees

주식회사 아웃스탠더스

Dates

Publication Date: 20260508
Application Date: 20250324
Priority Date: 20241029

Claims (20)

In terms of the method of providing laboratory evaluation services, An operation to calculate an evaluation score for each of the multiple research laboratories based on paper information of multiple research laboratories affiliated with each of the multiple universities and value information by academic society corresponding to the value of the multiple academic societies publishing the papers; An operation to provide a list showing the ranking of the plurality of laboratories based on the evaluation score of each of the plurality of laboratories. A method including
In paragraph 1, The paper information of the aforementioned multiple laboratories is, Information regarding the citation counts of papers published by each of the aforementioned multiple laboratories and the academic societies that published the said papers A method including
In paragraph 2, The operation of calculating the evaluation score for each of the aforementioned multiple laboratories is, The operation of calculating a paper score for papers published by each of the plurality of laboratories based on the number of citations of the above papers; and The operation of calculating the evaluation score of each of the aforementioned multiple laboratories through a neural network based on the aforementioned paper scores and the aforementioned value information for each academic society. A method including
In paragraph 3, The operation of calculating the above paper score is, An operation to adjust the citation count of the above papers; and The operation of calculating the paper scores for each of the aforementioned multiple laboratories by applying a decay rate based on the publication year of the aforementioned papers to the adjusted citation counts. A method including
In paragraph 4, The operation of adjusting the citation counts of the aforementioned papers is, An operation of analyzing the purpose of citing the above papers and applying a weight according to the said purpose to the number of citations of the said papers. A method including
In paragraph 4, The operation of adjusting the citation counts of the aforementioned papers is, An operation to calculate the internal laboratory citation rate for the said papers based on the relationship between the subject citing the said papers and the laboratory that published the said papers; and Action of adjusting the citation counts of the above papers based on the internal citation ratio of the above laboratory A method including
In paragraph 3, The operation of calculating the score of each of the plurality of laboratories through the above neural network is, The operation of performing a linear transformation on the above paper score; and An operation to calculate the evaluation score of each of the multiple laboratories by performing an attention operation on the linearly transformed paper scores and the value information for each academic society. A method including
In paragraph 1, The operation of providing a list showing the ranking of the aforementioned multiple laboratories is, The operation of classifying the above plurality of laboratories by research field; and The operation of providing a list showing the rankings of laboratories having the same research field, based on the evaluation scores of each of the laboratories having the same research field. A method including
In an electronic device providing laboratory evaluation services, processor; and Memory that stores instructions Includes, The above instructions are executed individually or collectively by the processor, causing the electronic device, Based on paper information of multiple research laboratories affiliated with each of multiple universities and value information by academic society corresponding to the value of multiple academic societies publishing papers, an evaluation score for each of the said multiple research laboratories is calculated, and An electronic device that provides a list showing the ranking of the plurality of laboratories based on the evaluation score of each of the plurality of laboratories.
In Paragraph 9, The paper information of the aforementioned multiple laboratories is, Information regarding the citation counts of papers published by each of the aforementioned multiple laboratories and the academic societies that published the said papers An electronic device including
In Paragraph 10, The above instructions are executed individually or collectively by the processor, causing the electronic device, Based on the citation counts of the above papers, paper scores for papers published by each of the above multiple laboratories are calculated, and An electronic device that calculates an evaluation score for each of the plurality of laboratories through a neural network based on the above paper score and the above academic society value information.
In Paragraph 11, The above instructions are executed individually or collectively by the processor, causing the electronic device, Adjust the citation counts of the above papers, and An electronic device for calculating the paper scores for each of the plurality of laboratories by applying a decay rate according to the publication year of the papers to the adjusted citation count.
In Paragraph 12, The above instructions are executed individually or collectively by the processor, causing the electronic device, An electronic device that analyzes the purpose of citing the above papers and applies a weight according to the purpose to the number of citations of the above papers.
In Paragraph 12, The above instructions are executed individually or collectively by the processor, causing the electronic device, Based on the relationship between the entities citing the aforementioned papers and the research labs that published the aforementioned papers, the internal lab citation rate for the aforementioned papers is calculated, and An electronic device that adjusts the number of citations of the papers based on the internal citation ratio of the above laboratory.
In Paragraph 11, The above instructions are executed individually or collectively by the processor, causing the electronic device, Perform a linear transformation on the above paper score, and An electronic device that calculates the score of each of the plurality of laboratories by performing an attention operation on linearly transformed paper scores and the value information for each academic society.
In Paragraph 9, The above instructions are executed individually or collectively by the processor, causing the electronic device, Classify the aforementioned multiple laboratories by research field, and An electronic device that provides a list showing the rankings of laboratories having the same research field, based on the evaluation scores of each of the laboratories having the same research field.
Regarding the method of training neural networks, An operation of calculating an evaluation score for each of the plurality of laboratories through the neural network, based on paper information of the plurality of laboratories affiliated with each of the plurality of universities and value information for each academic society corresponding to the value of the plurality of academic societies publishing the papers; The operation of predicting the rankings of the plurality of universities by adding the evaluation scores of laboratories belonging to the same university for each of the plurality of universities; and The operation of training the neural network by comparing the rankings of the aforementioned multiple universities with the actual values. A method including
In Paragraph 17, The operation of training the above neural network is, The operation of adjusting the parameters of the neural network through a backpropagation algorithm based on the difference between the predicted rankings of multiple universities and the actual values. A method including
In Paragraph 17, The operation of adjusting the parameters of the above neural network is, The operation of comparing the predicted rankings of multiple universities with the actual values by applying logarithmic weights. A method including
In Paragraph 17, The operation of adjusting the parameters of the above neural network is, An operation to update the parameter by applying hinge loss to the difference between the predicted rankings of the multiple universities and the actual values, so that the predicted rankings of the multiple universities and the actual values are minimized. A method including

Description

Method and device for providing lab assessment services, and method for training neural networks to provide lab assessment services The following disclosure relates to a method and apparatus for providing laboratory evaluation services, and a method for learning a neural network for providing laboratory evaluation services. Conventional university-level evaluation methods may struggle to reflect the actual research achievements of the various laboratories affiliated with a university. Consequently, there has been a problem where students seeking graduate school find it difficult to find information about these laboratories. The background technology described above is possessed or acquired by the inventor in the process of deriving the content of the disclosure of the present application, and cannot necessarily be considered as prior art disclosed to the general public prior to the filing of this application. FIG. 1 is an example of a system providing a laboratory evaluation service according to one embodiment. FIG. 2 is an example of an electronic device according to one embodiment. FIG. 3 is an example of a screen where a laboratory evaluation service is provided according to one embodiment. FIG. 4 is a diagram illustrating a method for calculating the evaluation score of a laboratory according to one embodiment. FIG. 5 is an example of a flowchart of a method for providing a laboratory evaluation service according to one embodiment. FIG. 6 is an example of a learning device according to one embodiment. FIG. 7 is an example of a flowchart of a learning method for a neural network according to one embodiment. Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, actual implementations are not limited to the specific embodiments disclosed, and the scope of this specification includes modifications, equivalents, or substitutions included in the technical concept described by the embodiments. Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component. When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or coupled with that other component, or that there may be other components in between. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to specify the existence of the described features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification. Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are given the same reference numeral regardless of the drawing number, and redundant descriptions thereof will be omitted. FIG. 1 is an example of a laboratory evaluation service system according to one embodiment. Referring to FIG. 1, a lab assessment service system (100) may include an electronic device (200) and a server (150). However, FIG. 1 is an example for explaining the present invention and should not be interpreted as limiting the scope of the present invention thereto. For example, the electronic device (200) may provide a lab assessment service to a user independently without a server (150). The electronic device (200) may be a smartphone, cellular phone, personal computer, laptop, notebook, netbook or tablet, personal digital assistant (PDA), digital camera, game console, MP3 player, personal multimedia player (PMP), e-book, navigation device, or home appliance, but is not limited thereto. The server (150) and the electronic device (200) can communicate using a network (not shown). For example, the network may include a Local Area Network (LAN), a Wide Area Network (WAN), a Value Added Network (VAN), a mobile radio communication network, a satellite communication network, and combinations thereof. The network is a comprehensive data communication network that enables the server